ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language

TY - CONF

T1 - ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language

AU - Sun, Zhaoyue

AU - Li, Jiazheng

AU - Pergola, Gabriele

AU - He, Yulan

N1 - Publisher Copyright: © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

PY - 2025/4/11

Y1 - 2025/4/11

N2 - Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and developed various models for extensive experiments and analysis. Our models can provide accurate explanations for unknown DDIs between known drugs. This paper contributes new tools to the field of DDI prediction and lays a solid foundation for further research on generating explanations for DDI predictions.

AB - Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and developed various models for extensive experiments and analysis. Our models can provide accurate explanations for unknown DDIs between known drugs. This paper contributes new tools to the field of DDI prediction and lays a solid foundation for further research on generating explanations for DDI predictions.

UR - http://www.scopus.com/inward/record.url?scp=105004001323&partnerID=8YFLogxK

U2 - 10.1609/aaai.v39i24.34709

DO - 10.1609/aaai.v39i24.34709

M3 - Paper

AN - SCOPUS:105004001323

SP - 25228

EP - 25236

T2 - 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

Y2 - 25 February 2025 through 4 March 2025

ER -